Modelling metabolic systems

ABSTRACT

A method of modelling a metabolic function of an individual is described, wherein a first ( 10 A) and second ( 10 B) set of data are input into a database ( 12 ). The first set of data ( 10 A) may constitute information of the diet of an individual, whereas the second set of data ( 10 B) may constitute information on the individual&#39;s activity. “Other” data ( 10 C) may also be input into the database ( 12 ). A third set of data relating to hormone activity within the body is also included. A modelling region ( 20 ) employs a plurality of mathematical models in order to provide an output function ( 30, 31 ) indicative of the time variation of a metabolic function of the individual.

The present invention relates to the modelling of metabolic systems, andin particular the modelling of metabolic systems in order to provideinformation on the time variation of a metabolic function.

Over the years, a number of researchers have attempted to produce modelsof certain aspects of metabolic systems. For example, there are researchteams working on mathematical models for diabetes in various countriesat this time. However, their work tends to be based on the chemistry: ofthe processes involved, with a target audience of clinicians.

Often, these models are highly complicated, and are not able to provideinformation in a format which is easily digestible or understandable.

Similarly, when these models are based on detailed input data, they areoften not suitable for use by untrained users, who may not be able tounderstand the equations, parameters, and variables involved. Inaddition, complicated input and analysis can be time-consuming, even forsomebody expertly qualified.

Persons newly diagnosed with diabetes often need time and guidance tocome to terms with the condition, and the limitations it can place ontheir lifestyle. Often, it will be necessary for a diabetic to undergosome exploratory research, in order to understand what their metabolicsystems can cope with. Similarly, clinicians or dieticians may need toinvestigate the way an individual's metabolism operates so that an idealinsulin regimen can be formulated.

Athletes, and those who wish to lose weight, may require the same typeof research in order to come up with information on the way diet andexercise effects their metabolism.

It would therefore be desirable to have a modelling system focused atpeople without clinical/dietary training as well as the healthcareprofessionals engaged in their day to day care.

Accordingly, it is one aim of the invention to provided a modellingsystem that simplifies the user interface.

It is a second aim of the invention to provide a modelling system thatallows a user to enter reduced amounts of data.

It is a third aim of the invention to provide a modelling system thatfacilitates the individual (or a healthcare professional) to model themetabolism of the individual with a view to improving their control oftheir condition, improving their quality of life in the short and/orlong term and/or improving their life expectancy.

Further aims and objects of the invention will become apparent from thefollowing description.

According to a first aspect of the invention there is provided themethod of modelling a metabolic function of an individual, comprisingthe steps of:

-   -   a) Inputting data into a database, including a first set of data        relating to the diet of the individual and a second set of data        relating to the activity of the individual, together with        additional data which may include date of birth, sex, height,        weight or any such data to be used later in the modelling        method;    -   b) Providing a third set of data relating to activity of one or        more hormones;    -   c) Employing a plurality of mathematical models that each        utilise the third set of data in conjunction with at least one        of the first set of input data and the second set of input data;    -   d) Providing an output function F1 indicative of the time        variation of a metabolic function of the individual.

The third set of data may comprise a set of default parameters relatingto the interaction of the one or more hormones with the individual.

In one embodiment, the method comprises the additional step of inputtingor importing data relating to the measurement of a variable in ametabolic system. This measured data is preferably compared to modelledvalues calculated by the output function.

The comparison may involve the calculation of an error, said error beingdefined as the difference in the measured and modelled values expressedover time.

The method may contain the additional step of modifying at least one ofthe default parameters included in the third data set in order to reducesaid error.

Preferably, this additional step is reiterated in order to minimise theerror.

The hormone may be insulin.

The output function is preferably selected from the group comprising:insulin levels in the blood, input of glucose from diet, input of fat,liver glucose reserves, fat reserves, muscle reserves, glucose outputdue to activity, rate of change of urine glucose, glucose used by thecentral nervous system, modelled blood glucose, and blood glucose error.

Values calculated by the output function may be displayed to the user.

The method may provide two or more output functions.

The method may be executed by a computer program.

According to a second aspect of the invention, there is provided acomputer program adapted to execute the method according to the firstaspect.

According to a third aspect of the invention, there is provided a methodfor predicting the effect of a change in diet or activity on a metabolicfunction of an individual, comprising the steps of:

-   -   a) executing the steps of the method of the first aspect of the        invention,    -   b) inputting data corresponding to a planned change in diet or        activity into a database,    -   c) executing one or more of the mathematical models utilising        the data corresponding to a planned change in diet or activity,    -   d) Providing an output function F2 indicative of the time        variation of a metabolic function for the planned change in diet        or activity.

Values calculated by the output function may be displayed to a user.

The method may comprise the additional step of comparing the outputfunctions F1 and F2 in order to provide information on the differenceeffected by the change in diet or activity. Optionally, valuescalculated by the output function F2 may be displayed to the user onlywhen a difference between output functions F1 and F2 is present.

The invention has particular, but not exclusive applications in:

-   -   The general care of people with Type 1 diabetes;    -   The general care of people with Type 2 diabetes;    -   The care of people with Type 1 or Type 2 diabetes with special        personal circumstances, such as:        -   (i) having a particularly active life,        -   (ii) having dietary limitations (eg vegan, food allergies),        -   (iii) who are over weight and wish to lose weight,        -   (iv) who have reduced hypoglycaemic awareness,        -   (v) who are ill.

The invention will help people with diabetes to control their conditionand avoid diabetic comas in the short term and complications in the longterm. It will also help non-diabetic people to balance their lifestyleand calorific intake, and thus help to control obesity. The inventionalso has applications in sports nutrition and other diet criticalconditions, such as cholesterol control and heart disease.

Individual elements of the invention will help people who for instance,wish to model their diet, activity or medications and the effect thesehave on their own metabolism.

In contrast to known systems, the invention regards human metabolism asa system and is based on a novel systems engineering and modellingapproach. This involves the modelling and analysis of energy input,storage and output under hormonal control. The following description isbased primarily on modelling the role of the hormone insulin, but thesame approach could be adapted for use with other hormones such asadrenaline and cortisol.

It is perhaps people with Type 1 diabetes that have the most pressingneed for good control of blood glucose levels, as the alternative is toface long term complications. Good control is achieved by balancingvarious parameters such as insulin dose, food intake, activity, bloodglucose levels, and the time relationships between these parameters. Itis desirable for a person with Type 1 diabetes to model their ownbiological system and adapt their insulin regimen, diet and activity toachieve good control. The present invention models these parameters byviewing the person as a system and employing a mathematical model foreach of these, and other parameters in order to determine how theyaffect the system. An important feature of the parameter models used inthe present invention is that they show how the parameter and itseffects vary with time.

Embodiments of the invention will now be described, by way of exampleonly, with reference to the following figures, of which:

FIG. 1 shows a block diagram of a method of modelling a metabolicfunction according to a first aspect of the invention.

FIG. 2 shows a block diagram of a method according to one embodiment ofthe invention, with modelling region shown in detail.

FIG. 3 shows a block diagram of a method including the input of measuredvalues.

FIG. 4 shows a block diagram of a method which includes the input of thefurther set of data.

FIG. 5 shows a block diagram of a modelling system according to anembodiment of the invention.

FIG. 6 illustrates how the modelling system shown in FIG. 5 can bedivided into separate, interacting sub-systems as follows:

FIG. 6 a shows the diet input sub-system

FIG. 6 b shows the activity input sub-system

FIG. 6 c shows the insulin input sub-system

FIG. 6 d shows the insulin generation sub-system

FIG. 6 e shows the liver sub-system

FIG. 6 f shows the fat sub-system

FIG. 6 g shows the blood glucose sub-system

FIG. 6 h shows the urine glucose sub-system

FIG. 6 i shows the muscles sub-system

FIG. 6 j shows the energy regulation sub-system

Referring firstly to FIG. 1, the invention in its first aspect includesthe input 10 of a first set of data 10 a and a second set of data 10 binto database 12. The first set of data 10 a constitutes information onthe diet of an individual. Typically, this comprises the input of thetype of food consumed, the amount consumed, and the time consumed. Thesecond set of data 10 b constitutes information on the individual'sactivity. Typically, this comprises the type of activity undergone, andthe period of time of the activity. Optionally, “other” data 10 c isinput into the database 12. This additional data may include date ofbirth, sex, height, weight, or any other such data to be used later inthe modelling method.

There is also provided a third set of data 16, relating to hormoneactivity within the body. This data comprises parameters whichcorrespond to the way an individual reacts to the presence of thehormone.

Modelling region 20 contains a series of mathematical models whichaccess data and database 12 and date from the third set of data 16. Themathematical models employed use the parameters relating to the hormoneactivity, in combination with the diet data from database 12 and/or theactivity data in database 12. Other data on the individual 10 c may alsobe used by the mathematical models. The modelling region provides one ormore output functions 30, 31, each of which represents the variation ofa particular metabolic function with a respective time. For example,output function F(t) may provide data on the variation of glucose levelsin the blood over time.

FIG. 2 shows a particular embodiment of the method of the inventionincluding the modelling region in detail. FIG. 2 relates to the dietmodelling portion of the method.

One embodiment of the invention requires the diet of the user to bedescribed in the form of number of grams of protein, carbohydrate,sugars, fat in each meal or snack consumed. To facilitate this theinvention uses tables of the composition of various food items, whichcan be added to by the user to reflect their diet. The user selects fooditems form this table, specifies the quantity of each item and so buildsthe menu of each meal and snack consumed. From this menu the requiredcomposition of each meal and snack consumed is determined for use by themodel.

The system allows the user to save, recall, edit and save as new, menusfor meals or snacks they consume on a regular basis. These featuresallow users to interact with the invention and input their diet data ina reasonable and time efficient manner. This aspect of the systemachieves an acceptable data input time for users.

The diet input data 10 a is stored in a diet table 12 a within thedatabase 12. A preliminary step provides a plurality of time courses22(a) to (e) corresponding to the time variation of fat, protein, highcarbohydrates, medium carbohydrates, low carbohydrates, input into thesystem. This step is carried out by simple calculations based on thenutrient content of the foods consumed according to diet input 10 a.

For example, a user consumes a meal at time t=0, the meal comprising Pgrams of protein, H grams of fast acting carbohydrates (sugars), M gramsof medium acting carbohydrates (carbohydrates—sugars), and F grams offat (sum of saturated and unsaturated fat).

Time courses 22(a) to 22(e) represent the temporal variation of input ofthese nutrients. The model is able to estimate the approximate calorieinput from the quantities of fat, protein, and carbohydrate into themetabolic system.

This particular method provides output functions corresponding to thetime variation of fat reserves, Fr(t) (the lymphatic system), and thetime variation of liver reserves, Lr(t) (the hepatic system). Thus thecalorie input into both the lymphatic system and the hepatic system ismodelled.

The different carbohydrate components are considered to act in discretetime intervals. The fast-acting carbohydrate component is considered toact 30-60 minutes after the meal consumption, and the calories inputinto the hepatic system in each ΔT time interval, d_(glu) is:d _(glu)=4HΔT/30

The medium-acting carbohydrate component is considered to act 60-240minutes after the meal consumption, and the calories input into thehepatic system in a ΔT time interval in this period is:d _(glu)=4MΔT/60

The protein model 24 b breaks up the protein component into glucose(60%) and fat (40%), and these sub-components can subsequently enter thehepatic and lymphatic routes during the 120-240 minute interval afterconsumption of the meal. Thus, the hepatic input is modelled as:d _(glu)=0.6*4PΔT/120and the input into the lymphatic system is:d _(fat)=0.4*4PΔT/120.

The fat model 24 a uses data in the fat time course and data from theprotein model to model the calorie input into the lymphatic system 26 a.The model estimates the time of the input from the fat component asbeing after 120 minutes from the meal consumption. However, an extrafactor is included in the fat model to take into account the percentagefat content of the meal, and how it can affect the duration andamplitude of release into the lymphatic system. If greater than 30% ofthe total calories come from fat, then the amplitude of release isscaled down, and the duration of release is scaled down. That is:

If 9*F/C>0.3, where C=4*(P+H+M)+9*F, then the duration of release isestimated as 120 to X minutes after the meal consumption with X iscalculated as:X=9F120/(0.3*C)

The calorie input into the lymphatic system, d_(fat), is modelled as:d _(fat)=0.3CΔT/X

If the calories from fat comprise less than 30% of the total calories,then the release period is given as 120 minutes to 240 minutes afterconsumption, and:d _(fat)=9FΔT/120

In this model high carbohydrates, medium carbohydrates, lowcarbohydrates and 60% of the protein are all modelled differently due totheir differing action times within the body.

Module 27 accounts for diet-induced thermogenesis (DIT) and growth. DITis the production of heat due to the food eaten and accounts for thesynthesis of enzymes that digest the food and the energy utilised byabsorption processes. This accounts for 8 to 10% of the metabolisableenergy intake. DIT has been implemented in the model by reducing theglucose and fats arising at the gut wall by a particular factor. Inaddition, the model makes an estimate for the amount of dietary intakeutilised for growth and repair.

DIT and growth have been accounted for as follows. The expressionsd_(fat) and d_(glu) are modified by a factor according to the followingequations to give the actual calories digested and available forabsorption from the get, dggut (hepatic system), and dfgut (lymphaticsystem)dggut=d _(glu)*(1−(DIT+GROWTH))dfgut=d _(fat)*(1−(DIT+GROWTH))

Further modelling takes place, namely to model the way that glucose isabsorbed from the gut wall into the hepatic system, and subsequent inputinto the liver reserves. In this way, an output function of the timevariation of the liver reserves Lr(t)is calculated, as is the timevariation of fat reserves Fr(t).

FIG. 2 illustrates the complexity of the modelling system. It can beseen that a number of mathematical models are used at various stages ofthe modelling process, in order to provide one or more time variableoutput functions. It is evident that further output functions could bedisplayed according to the application of the modelling method. Forexample, 28 provides data on glucose at the gut wall Ggut(t), and ifrequired this information could be presented to the user of the system,e.g. as a print out or in graphical form.

FIG. 3 shows a block diagram of an alternative embodiment of theinvention. This embodiment is improved in the sense that certain modelparameters are evolved to fit the model to a particular user.

Parameters used within the invention can be divided into two categories.Firstly, there are those which are the same for all users which arebased on chemical constants etc. Secondly, there are those which aredifferent for each user, and are located in a user interface tablewithin the database. The values of these parameters are originally givena default value within the third data set, but for improved resultsthese parameters need to be fitted to each individual user.

As can be seen from FIG. 3, there is provided an additional input 17 forinputting or importing measured values into the system. These measuredvalues may correspond to, for example, blood glucose levels taken atdiscrete time intervals. Output function 30 is calculated according tothe general principles of FIG. 1. In this case, the output functiongives the time variation of the blood glucose levels based on data input10 a, 10 b and parameters held within the third data set.

A comparison module is provided in order to compare the results of thecalculated function and the directly measured values from input 17. Atthe times at which the measured values are taken, the value for an erroris calculated by subtracting the recorded value of blood glucose levelfrom the modelled value, and expressing as a percentage of the bloodglucose value. Thus, error function E(t) 42 is provided.

Incorporated into this embodiment is an optimisation step 50. Theoptimisation module accesses default parameters from the third data setand changes the values one by one. Output function F(T) is recalculatedusing the modified parameters and the comparison module 40 againcompares the measured values with the modelled values, to provide thenew error function E(T). The optimisation module then determines anincrease or decrease in the error function E(T) and the process isrepeated. Reiteration of this process enables the parameters used fromthe third data set, to evolve to the individual in question. Byminimising the error function, it is possible to provide a morerealistic model of hormone activity in an individual.

There will now be described by way of example a particular embodimentbeing adapted to model the blood glucose levels of a Type 1 diabetic.

FIG. 4 shows a block diagram of a modelling system, similar to that ofFIG. 3, but with an additional input 10′. This input is for enteringdata relating to preparations taken by the individual that effect thehormone levels. For example, in the case of the diabetic modellingsystem input 10′ will include the entering of insulin doses taken by theindividual. In alternative applications input 10′ may includeinformation on drug intake, or the intake of other specific hormones.

Modelling region 20 contains a series of mathematical models designedfor predicting the activity of the insulin in the body. A number offactors accounted for can be seen in FIG. 5, which shows the interactionof various models in a diabetic user. This system can be described as aseries of interacting subsystems as shown in FIGS. 6 a to 6 j.

FIG. 6 c shows that the insulin doses input is stored in an insulindoses table within the main database. Loss of insulin at the surface andin the body tissue is accounted for before the main insulin model isemployed. More information about the insulin model will be providedbelow, but it is the intention to first outline the principles accordingto this aspect of the invention.

This embodiment uses a complex and extended model which models variousinsulin types, examples follow:

-   -   Type 1: Actrapid    -   Type 2: Protophane    -   Type 3: Monotard    -   Type 4: Ultratad    -   Type 5: Humalog    -   Type 10: Human Mixtard 10    -   Type 20: Human Mixtard 20    -   Type 30: Human Mixtard 30

There is a delay between the time of injection of the insulin and thetime at which its action commences. This delay is dependent upon thetype of insulin and the individual person with diabetes. In the model,the delay is incremented as a fixed, nominal value for a particular typeof insulin, multiplied by a factor that is both insulin and userdependent. This allows the action time to reach the individual insulintimes to be customised to each user.

There is a user dependent insulin sensitivity associated with each typeof insulin. This allows the action of each insulin to be customised toeach user.

There is a user dependent insulin elimination rate associated with eachtype of insulin. This allows the elimination of each insulin to becustomised to each user.

All of the parameters are given initial, default values held within thethird data set. The model is then run to give an initial start point, toprovide an output function 30. As described above, the values calculatedby the output function are directly compared to the measured values bythe comparison function, to calculate an error function E(T). That is,the values of the modelled glucose levels are compared to the measuredblood glucose levels to provide a blood glucose error function.

By varying the values of the parameters above, it is possible to reducethe value of the blood glucose error function. This can be done bystandard optimisation techniques. For example for a cycle which modifiesthe parameter value, recalculates the modelled blood glucose function,compares the function to the measured values and recalculates the bloodglucose error.

For the evolution process to operate with a reasonable possibility ofsuccess, it is recommended that the user provides a few days of nominaldata. Nominal data is regarded as a period when the user isparticipating in the nominal routine of diet and exercise, and is notsuffering from any ailments that will compound or complicate the model.

There is now described, an example of an insulin model which may bedeployed in an example embodiment of the invention being applied to aninsulin dependent diabetic. The insulin input sub-system is illustratedin FIG. 6 c.

Plasma insulin for a Type 1 diabetic is defined as A(t), and is effectedby a dose of D units which begins to have an effect at time t=0.

Rate of change of plasma insulin dA(t)/dt has two components,corresponding to an absorption process (first, positive term) and anelimination process (second, negative term), given by the equation:$\quad{\frac{\mathbb{d}{A(t)}}{\mathbb{d}t} = {\frac{s \cdot t^{s} \cdot \left( T_{50} \right)^{s} \cdot D}{t \cdot \left( {\left( T_{50} \right)^{s} + t^{s}} \right)^{2}} - {k_{e} \cdot {A(t)}}}}$where T₅₀ = a ⋅ D + bs, a and b (and hence T₅₀) are parameters that depend on the type ofinsulin being used. The insulin elimination rate, K_(e), may depend onthe type of insulin being used.

As explained above, there is a delay between the time of injection ofthe insulin and the time at which its action commences (defined as t=0above). This delay is dependent on the type of insulin and theindividual person with diabetes. The delay is implemented as a fixed,nominal value for a particular type of insulin multiplied by a factorthat is both insulin and patient dependent. This allows the action timesof each of the individual insulin types to be customised to each user.Insulin types currently modelled are listed in the table below: Insulintype no 1 2 3 4 5 Novo Actrapid Protophane Monotard Ultratard nordiskname Lilly Humalin S Humalin I Humalin Humalog Name Lente GenericRegular NPH Lente Ultralente Lyspro Name Other Velosulin Insulatardnames S 2 2 2.4 2.5 2 a(hours 0.05 0.18 0.15 0 0.05 per unit) b(hours)1.7 4.9 6.2 13 1.7 K_(e) (per 5.4 5.4 5.4 5.4 5.4 hour) Nominal 45 105105 240 0 delay (minutes) Duration 16 24 24 36 16 (hours)

No data on Humalog was available at the time of writing, these valuesare based on actrapid with zero time delay, as a first approximation.

The various forms of mixtard, such as human mixtard 30, are modelled ascombinations of doses of type 1 and type 2 insulin from the above table.

The generation of insulin in the pancreas is also modelled, according tothe following equation:$\frac{\mathbb{d}{i(t)}}{\mathbb{d}t} = {{\gamma \cdot \left\lbrack {{g(t)} - h} \right\rbrack^{+}} - {k_{e} \cdot {i(t)}}}$where i(t) is the generated insulin concentration, g(t) is the bloodglucose concentration—in grams per litre, and h is a threshold value ofblood glucose. The insulin generation sub-system is shown in FIG. 6 d.

The value of γ used is 3.37*35E—3/0.18=0.655278 hours⁻¹

An initial, basal, level of insulin concentration, i(0), is alsorequired. In the model a value of 15 mU/litre is assumed.

The amount of insulin generated is determined by a the user dependentinsulin production parameter. This is expressed as a percentage (0%=noinsulin production, totally diabetic, 100%=non-diabetic). The value ofthe insulin production parameter is determined from the users date ofbirth, sex, height and weight, which gives an expected insulinrequirement, and their nominal daily insulin dose. Any difference is dueto insulin production, which can then be quantified.

With these values in place the insulin values generated fornon-diabetics resemble those described by other authors.

The output from the input and generated insulin models are combined toform a model of the insulin concentration in the users blood. Thiscombination takes into account the effect of the user dependent insulinsensitivity parameter on input insulin, and the user dependent insulinproduction parameter on the insulin produced.

The activity model utilises a table of adult and child activities in theactivity input system shown in FIG. 6 b. These contain the PhysicalActivity Ratio (Base metabolic rate multiplying factor) for eachactivity. Users specify activities by the name of the activity, itsstart time and duration. From this and the users birth date, sex, heightand weight the calories consumed by that user in that duration ofactivity can be determined.

The user describes their activity in a day as follows, firstly theydefine when they woke up and when they went to bed. The model then usestheir sleeping metabolic rate do determine how many calories are beingused per minute while the user is asleep and awake, but not engaged instrenuous activity. During the day users report any activities thataccelerate their base metabolic rate, based on the physical activityratios in the activity tables described earlier. The additional caloriesused per minute during each activity is then determined and added to theenergy demand by the user for that day.

The liver sub-system, shown in FIG. 6 e, accepts inputs of glucose fromthe gut wall to recharge the liver reserves, and from the fat reservesduring gluconeogenesis, when the liver reserves are low to supplementthem. The liver outputs glucose to the blood either from liver reservesor fat gluconeogenesis. The liver action, at any point in time, isdetermined by blood insulin level, blood glucose level, food input fromthe gut and the status of the liver reserves. The liver actions theflowing processes: input to reserves, output from reserves, enablegluconeogenesis, and disable gluconeogenesis.

The fat sub-system, shown in FIG. 6 f, accepts fat input from the diet,and also can store surplus blood glucose as fat under certainconditions. Body fat can be called on to meet the energy demands ofactivity directly, and to provide fuel via gluconeogenesis in the liverif the liver reserves become depleted.

The blood glucose sub-system, shown in FIG. 6 g, accepts glucose fromthe liver system, either from food surplus to the liver's needs, theliver reserves of gluconeogenesis in the liver. Once in the bloodglucose can leave independently of the blood insulin concentration tofuel fundamental body functions, such as the brain and central nervoussystem. It can leave in a manner dependent on blood insulinconcentration to the muscles and for any surplus to be saved as bodyfat. If the blood glucose level exceeds the renal threshold (9 mmol/l)the kidneys commence removal of some blood glucose via the urine.

The urine glucose sub-system, shown in FIG. 6 h, allows the body toattempt to bring down high levels of blood glucose. If the blood glucoselevel exceeds the renal threshold (9 mmol/l) the kidneys commenceremoval of some blood glucose via the urine.

The muscles sub-system, shown in FIG. 6 i, allows glucose released fromthe blood by the action of insulin in the blood to replenish muscleglycogen stores that have been depleted by activity.

The energy regulation sub-system, shown in FIG. 6 j, allows the energydemands of the body, as determined by the activity input sub-system, tobe met. The body has several possible sources of fuel, fat, muscleglycogen stores, and blood glucose released independently of bloodinsulin concentrations. Which fuel source is used, and how much isrelated to, for instance, the demand, the state of some of the fuelsources (particularly the blood glucose and liver glycogen store).

The above subsystems are modelled by utilising various equations andmathematical models. Indeed, each transition from one data table toanother table or function is implemented by a mathematical model orequation. The equations used may be any suitable for modelling themetabolic system in question.

It will be apparent to one skilled in the art that various modificationsand adaptations to the described system are possible within the scope ofthe invention. The invention is not limited to particular equations usedin the modelling method. As research continues, it is envisaged thatimproved mathematical representations may be incorporated into theinvention.

The present invention allows a person with diabetes, or a person thatcares for diabetes sufferers to experience an improvement in diabeticcontrol by gaining a better understanding of the condition and howvarious parameters are affected. This results in an improvement in thelife style for the person with diabetes and those caring for them.,

The invention allows the exploration of so-called “what if” scenarios,eg “what happens if the person with diabetes misses a snack beforeexercise?”, or “could better control have been achieved if the personwith diabetes had eaten their snack sooner/later, undergone a differenttype of activity, or reduced/increased their insulin dose”.

By inputting data corresponding to a planned snack or activity, theeffect on the blood glucose levels (or other metabolic functions) can bepredicted. The user can quickly see the effects of adding or removing asnack without entering a large amount of data. Further, the method mayselectively display the data, such that the user sees the predictedresults only when a the planned snack or activity would cause asignificant change to the blood glucose levels.

It should be noted that the same benefits apply to non-diabeticindividuals who wish to model particular aspects of the metabolism.

Further modifications and improvements may be incorporated withoutdeparting from the scope of the invention herein intended.

1. A method of calculating the time variation of a metabolic parameterof an individual, the method comprising the steps of: (a) Inputting datainto a database, the data including diet data relating to the diet ofthe individual and activity data relating to the activity of theindividual, and personal data of the individual constituting one or moreof date of birth, sex, height, or weight; (b) Providing hormoneinteraction parameters relating to activity of one or more hormones; (c)Calculating using a plurality of mathematical models, the time variationof a metabolic parameter, wherein the mathematical models utilize thehormone interaction parameters in conjunction with the diet data and/orthe activity data, and model the energy stores, being the blood glucose,liver reserves, fat reserves, and muscle glycogen reserves of the bodyof the individual in calculating the time variation of a metabolicparameter.
 2. The method as claimed in claim 1, wherein the step ofcalculating the time variation of the metabolic parameter includesdetermining which of the energy stores are used to meet energy demandsfrom the state of the energy stores.
 3. The method as claimed in claim1, wherein the step of calculating the time variation of the metabolicparameter models the liver action of the individual, as determined byblood insulin level, blood glucose level, food input from the gut andstatus of the liver reserves.
 4. The method as claimed in claim 1,wherein the hormone interaction parameters comprises a set of defaultparameters relating to the interaction of the one or more hormones withthe individual.
 5. The method as claimed in claim 4, wherein the hormoneinteraction parameters comprise one or more of the followinguser-dependent parameters: an insulin activity delay parameter, aninsulin sensitivity parameter, an insulin elimination rate parameter, aninsulin production parameter.
 6. The method as claimed in claim 1comprising the additional step of inputting or importing measurementdata relating to the measurement of a variable in a metabolic system. 7.The method as claimed in claim 6, comprising the step of comparing themeasurement data with calculated values of the metabolic parameter.
 8. AThe method as claimed in claim 7, wherein the step of comparingcomprises the sub-step of calculating of an error function said errorfunction being defined as the difference in the measurement data valuesand the calculated metabolic parameter values over time.
 9. The methodas claimed in claim 8, comprising the additional steps of modifying atleast one of the default parameters included in the hormone interactionparameters, recalculating the time variation of the metabolic parameterusing the modified parameter, and recalculating the error function. 10.The method as claimed in claim 9, wherein the additional steps ofmodifying at least one of the default parameters included in the hormoneinteraction parameters, recalculating the time variation of themetabolic parameter using the modified parameter and recalculating theerror function are reiterated in order to minimize the error.
 11. Themethod as claimed in claim 1, wherein the data is the blood glucoselevel.
 12. The method as claimed in claim 1, wherein the hormone isinsulin.
 13. The method as claimed in claim 1 comprising the step ofentering into the database data relating to insulin doses taken by theindividual.
 14. The method as claimed in claim 1 comprising theadditional steps of selecting food items from a table of food itemsspecifying a quantity of each food item, and determining the nutritionalcomposition of food consumed.
 15. The method as claimed in claim 14comprising the additional step of calculating a plurality of timecourses corresponding to the time variation of fat, protein, andcarbohydrates input into the metabolic system.
 16. The method as claimedin claim 1, wherein the input of activity data includes the sub-steps ofselecting an activity from a table of activities, and specifying a starttime and duration of the activity.
 17. The method as claimed in claim 16comprising the step of determining the calories consumed by theindividual user during the activity from the activity data a basalmetabolic rate determined from the birth date, sex, height, and weightof the individual and a basal metabolic rate multiplying factorassociated with the activity.
 18. The method as claimed in claim 1,wherein the metabolic parameter is selected from the group comprising:insulin levels in the blood, input of glucose from diet, input of fat,liver glucose reserves, fat reserves, muscle reserves, glucose outputdue to activity, rate of change of urine glucose, glucose used by thecentral nervous system, modelled blood glucose, and blood glucose error.19. The method as claimed in claim 1, comprising the step of displayingcalculated values to a user.
 20. A method for calculating the effect ofa change in diet, activity or insulin dose taken on the time variationof a metabolic parameter of an individual, the method comprising thesteps of: a) Calculating the time variation of a metabolic parameter; b)Inputting data corresponding to a planned or retrospective change indiet, activity or insulin dose into a database; c) Calculating, using aplurality of mathematical models, the time variation of a metabolicparameter, wherein the mathematical models utilize the hormoneinteraction parameters in conjunction with the data corresponding to aplanned or retrospective change in diet, activity or insulin dose takenand model the blood glucose levels, liver reserves, fat reserves, andmuscle glycogen reserves of the body of the individual in calculatingthe time variation of a metabolic parameter for the planned orretrospective change in diet or activity, or insulin dose taken.
 21. Themethod as claimed in claim 20 comprising the additional step ofdisplaying values of the metabolic parameter to a user.
 22. The methodas claimed in claim 20 comprising the additional step of comparing thetime variation of the metabolic parameter with the time variation of ametabolic parameter for a planned or retrospective change in diet,activity, or insulin dose, in order to provide information on thedifference effected by the planned or retrospective change in diet,activity or insulin dose taken.
 23. The method as claimed in claim 20,wherein the method is executed by a computer program.
 24. A computerprogram adapted to execute the method claimed in claim 20.